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Overwriting output/custom.css
import sys
sys.path.append('./python/')
from mayavi import mlab
from mayavi.tools.sources import vector_field, scalar_field
mlab.options.offscreen = True
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
import astropy.units as u
import sunpy.map
import yt
import pysac.yt
%matplotlib inline
font_size = 20
pgf_with_latex = {
"font.size": font_size,
"axes.labelsize": font_size, # LaTeX default is 10pt font.
"legend.fontsize":font_size + 5,
"xtick.labelsize": font_size,
"ytick.labelsize": font_size,
"ytick.labelsize": font_size,
"savefig.transparent": True,
"animation.html": "html5"
}
matplotlib.rcParams.update(pgf_with_latex)
WARNING:traits.has_traits:DEPRECATED: traits.has_traits.wrapped_class, 'the 'implements' class advisor has been deprecated. Use the 'provides' class decorator.
How is the solar atmosphere heated?
How is the energy transported from the top of the convection zone / photosphere upwards?
How is the Energy Deposited?
"The challenge remains to understand how, and where, these waves are generated and dissipated in the solar atmosphere, [...]" - (McIntosh et al. 2011)
x_range, y_range = [-300, 300]*u.arcsec, [-250, 250]*u.arcsec
plt.ioff()
fig = plt.figure(dpi=50, figsize=(11,8))
mm = sunpy.map.Map('/home/stuart/VivaData/gband_data/Gband_cospatial_cotemporal_00000.fits').submap(x_range, y_range)
mm = mm.submap([-440,440]*u.arcsec, [-440,440]*u.arcsec)
mm.plot_settings['cmap'] = 'gray'
mm.plot_settings['title'] = ''
im = mm.plot()
fig.savefig('./images/gband-plot.png', transparent=True)
The dynamic photosphere with embedded magnetic field provides many potential ways of driving MHD waves.
The code used is the Sheffield Advanced Code (SAC) (Shelyag, Fedun, and Erdélyi 2008).
SAC is based on the Versatile Advection Code (VAC) (Tóth 1996).
SAC simulates perturbations on a static background, using a CD4 solver with hyper-diffusion and hyper-viscosity terms to stabalise the solution.
This makes it well-suited to solving wave perturbations on top of a highly stratified background such as the solar atmosphere.
To simulate wave excitation in the photosphere a numerical model of the solar atmosphere is needed.
Hydrostatic background from the VAL III model C (Vernazza, Avrett, and Loeser 1981):
import pysac.mhs_atmosphere as atm
#Read in the VAL3C model
empirical_data = atm.hs_atmosphere.read_VAL3c_MTW(MTW_file=False)
# Create a Z array at the interpolated resolution and interpolate.
ZZ = u.Quantity(np.linspace(empirical_data['Z'][0], empirical_data['Z'][-1], 128), unit=empirical_data['Z'].unit)
table = atm.hs_atmosphere.interpolate_atmosphere(empirical_data, ZZ, s=0)
# Create a figure and make space for the axes.
fig, ax = plt.subplots(gridspec_kw={'right':0.77, 'left':0.16, 'bottom':0.13}, figsize=(13,8))
# Shortcut all the Mm conversion.
Z = empirical_data['Z'].to(u.Mm)
lrho, = ax.plot(Z, empirical_data['rho'].quantity.si, 'x', color='blue')
lrho_i, = ax.plot(ZZ.to(u.Mm), table['rho'].quantity.si, color='blue')
ax2 = ax.twinx()
lp, = ax2.plot(Z, empirical_data['p'].to(u.Pa), 'x', color='green')
lp_i, = ax2.plot(ZZ.to(u.Mm), table['p'].to(u.Pa), color='green')
ax3 = ax.twinx()
ax3.spines["right"].set_position(("axes", 1.2))
lt, = ax3.plot(Z, empirical_data['T'].to(u.K), 'x', color='red')
lt_i, = ax3.plot(ZZ.to(u.Mm), table['T'].to(u.K), color='red')
# Set primary axes properties and labels
ax.semilogy()
ax.set_ylabel(r"Density [{}]".format(lrho._yorig.unit._repr_latex_()))
ax.set_xlabel(r"Height [{}]".format(lrho._xorig.unit._repr_latex_()))
ax.set_xlim(Z[0].value-0.1, Z[-1].value+0.1)
# Pressure Axis
ax2.semilogy()
ax2.set_ylabel(r"Pressure [{}]".format(lp._yorig.unit._repr_latex_()))
# Temp axis
ax3.set_ylabel(r"Temperature [{}]".format(lt._yorig.unit._repr_latex_()))
ax.set_xlim([-0.02,1.62])
ax3.set_ylim([3500,7500])
# Set the colours for the ticks and the labels.
ax.tick_params(axis='y', colors=lrho.get_color())
ax2.tick_params(axis='y', colors=lp.get_color())
ax3.tick_params(axis='y', colors=lt.get_color())
ax.yaxis.label.set_color(lrho.get_color())
ax2.yaxis.label.set_color(lp.get_color())
ax3.yaxis.label.set_color(lt.get_color())
fig
from pysac.mhs_atmosphere.parameters.model_pars import mfe_setup as model_pars
import pysac.mhs_atmosphere as atm
# Cheeky Reset to Photosphere
model_pars['xyz'][4] = 0*u.Mm
#==============================================================================
# Build the MFE flux tube model using pysac
#==============================================================================
# model setup
scales, physical_constants = atm.units_const.get_parameters()
option_pars = atm.options.set_options(model_pars, False, l_gdf=True)
coords = atm.model_pars.get_coords(model_pars['Nxyz'], u.Quantity(model_pars['xyz']))
#interpolate the hs 1D profiles from empirical data source[s]
empirical_data = atm.hs_atmosphere.read_VAL3c_MTW(mu=physical_constants['mu'])
table = atm.hs_atmosphere.interpolate_atmosphere(empirical_data, coords['Zext'])
table['rho'] += table['rho'].min()*3.6
# calculate 1d hydrostatic balance from empirical density profile
# the hs pressure balance is enhanced by pressure equivalent to the
# residual mean coronal magnetic pressure contribution once the magnetic
# field has been applied
magp_meanz = np.ones(len(coords['Z'])) * u.one
magp_meanz *= model_pars['pBplus']**2/(2*physical_constants['mu0'])
# Make the vertical profile 3D
pressure_z, rho_z, Rgas_z = atm.hs_atmosphere.vertical_profile(coords['Z'], table, magp_meanz,
physical_constants, coords['dz'])
# Generate 3D coordinate arrays
x, y, z = u.Quantity(np.mgrid[coords['xmin']:coords['xmax']:1j*model_pars['Nxyz'][0],
coords['ymin']:coords['ymax']:1j*model_pars['Nxyz'][1],
coords['zmin']:coords['zmax']:1j*model_pars['Nxyz'][2]], unit=coords['xmin'].unit)
# Get default MFE flux tube parameters out of pysac
xi, yi, Si = atm.flux_tubes.get_flux_tubes(model_pars, coords, option_pars)
# Generate the 3D magnetic field
allmag = atm.flux_tubes.construct_magnetic_field(x, y, z, xi[0], yi[0], Si[0], model_pars, option_pars,
physical_constants, scales)
pressure_m, rho_m, Bx, By ,Bz, Btensx, Btensy = allmag
# local proc 3D mhs arrays
pressure, rho = atm.mhs_3D.mhs_3D_profile(z, pressure_z, rho_z, pressure_m, rho_m)
magp = (Bx**2 + By**2 + Bz**2) / (2.*physical_constants['mu0'])
energy = atm.mhs_3D.get_internal_energy(pressure, magp, physical_constants)
#### YT STUFF ####
magnetic_field_x = lambda field, data: data['mag_field_x']
yt.add_field(("gas","magnetic_field_x"), function=magnetic_field_x, units=yt.units.T.units)
magnetic_field_y = lambda field, data: data['mag_field_y']
yt.add_field(("gas","magnetic_field_y"), function=magnetic_field_y, units=yt.units.T.units)
magnetic_field_z = lambda field, data: data['mag_field_z']
yt.add_field(("gas","magnetic_field_z"), function=magnetic_field_z, units=yt.units.T.units)
# Add derived Fields
def magnetic_field_strength(field, data):
return np.sqrt(data["mag_field_x"]**2 + data["mag_field_y"]**2 + data["mag_field_z"]**2)
yt.add_field(("gas","magnetic_field_strength"), function=magnetic_field_strength, units=yt.units.T.units)
#def alfven_speed(field, data):
# return np.sqrt(2.*data['magnetic_pressure']/data['density'])
#yt.add_field(("gas","alfven_speed"), function=alfven_speed, units=(yt.units.m/yt.units.s).units)
bbox = u.Quantity([u.Quantity([coords['xmin'], coords['xmax']]),
u.Quantity([coords['ymin'], coords['ymax']]),
u.Quantity([coords['zmin'], coords['zmax']])]).to(u.m).value
# Now build a yt DataSet with the generated data:
data = {'mag_field_x':yt.YTQuantity.from_astropy(Bx.decompose()),
'mag_field_y':yt.YTQuantity.from_astropy(By.decompose()),
'mag_field_z':yt.YTQuantity.from_astropy(Bz.decompose()),
'pressure': yt.YTQuantity.from_astropy(pressure.decompose()),
'magnetic_pressure': yt.YTQuantity.from_astropy(magp.decompose()),
'density': yt.YTQuantity.from_astropy(rho.decompose())}
ds = yt.load_uniform_grid(data, x.shape, length_unit='m', magnetic_unit='T',
mass_unit='kg', periodicity=[False]*3, bbox=bbox)
/home/stuart/GitHub/SWAT/pysac/pysac/mhs_atmosphere/mhs_model/flux_tubes.py:312: Warning: pbbal.max() = -0.111891563752 kg / (m s2)
warnings.warn("pbbal.max() = {}".format(pbbal.max().decompose()), Warning)
yt : [INFO ] 2015-12-04 14:48:45,830 Parameters: current_time = 0.0
yt : [INFO ] 2015-12-04 14:48:45,831 Parameters: domain_dimensions = [129 129 128]
yt : [INFO ] 2015-12-04 14:48:45,832 Parameters: domain_left_edge = [-1000000. -1000000. 0.]
yt : [INFO ] 2015-12-04 14:48:45,834 Parameters: domain_right_edge = [ 1000000. 1000000. 1587786.3]
yt : [INFO ] 2015-12-04 14:48:45,835 Parameters: cosmological_simulation = 0.0
The magnetic field model mimics a Magnetic Bright Point (MBP) and follows (Gent, Fedun, Mumford and Erdélyi 2013) and is constructed as a self-similar (Schlüter and Temesváry 1958; Low 1980), cylindrically symmetric, expanding field.
slc = yt.SlicePlot(ds, 'x', 'density', origin='lower-center-domain', axes_unit='Mm')
slc.set_figure_size(9)
slc.set_cmap('all', 'viridis')
slc.set_font_size(20)
slc.set_zlim('all', 0, 2.5e-4)
seed_points = np.zeros([11,2]) + 1.52
seed_points[:,0] = np.linspace(-0.99, 0.95, seed_points.shape[0], endpoint=True)
slc.annotate_streamlines('mag_field_y', 'mag_field_z', field_color='magnetic_field_strength',
plot_args={'start_points':seed_points, 'density':15, 'cmap':'Blues', 'linewidth':2,
'norm': matplotlib.colors.LogNorm(*ds.all_data().quantities.extrema("magnetic_field_strength"))})
#force render
slc.save('/tmp/test.png')
#use the raw Figure for transparent bg
slc.plots['density'].figure
yt : [INFO ] 2015-12-04 14:48:45,929 Loading field plugins.
yt : [INFO ] 2015-12-04 14:48:45,931 Loaded angular_momentum (8 new fields)
yt : [INFO ] 2015-12-04 14:48:45,932 Loaded astro (15 new fields)
yt : [INFO ] 2015-12-04 14:48:45,932 Loaded cosmology (22 new fields)
yt : [INFO ] 2015-12-04 14:48:45,934 Loaded fluid (63 new fields)
yt : [INFO ] 2015-12-04 14:48:45,935 Loaded fluid_vector (95 new fields)
yt : [INFO ] 2015-12-04 14:48:45,936 Loaded geometric (111 new fields)
yt : [INFO ] 2015-12-04 14:48:45,937 Loaded local (115 new fields)
yt : [INFO ] 2015-12-04 14:48:45,937 Loaded magnetic_field (121 new fields)
yt : [INFO ] 2015-12-04 14:48:45,938 Loaded my_plugins (121 new fields)
yt : [INFO ] 2015-12-04 14:48:45,939 Loaded species (123 new fields)
yt : [INFO ] 2015-12-04 14:48:46,276 xlim = -1000000.000000 1000000.000000
yt : [INFO ] 2015-12-04 14:48:46,276 ylim = 0.000000 1587786.300000
yt : [INFO ] 2015-12-04 14:48:46,279 xlim = -1000000.000000 1000000.000000
yt : [INFO ] 2015-12-04 14:48:46,280 ylim = 0.000000 1587786.300000
yt : [INFO ] 2015-12-04 14:48:46,290 Making a fixed resolution buffer of (('gas', 'density')) 800 by 800
yt : [WARNING ] 2015-12-04 14:48:46,343 Plot image for field ('gas', 'density') has both positive and negative values. Min = -0.000032, Max = 0.000267.
yt : [WARNING ] 2015-12-04 14:48:46,344 Switching to symlog colorbar scaling unless linear scaling is specified later
yt : [INFO ] 2015-12-04 14:48:47,927 Saving plot /tmp/test.png

Photospheric drivers excite multiple wave modes simulatenously.
How to quantify the relative strengths of the different modes from different drivers?
Assume uniform media:
#Define tvtk notebook viewer
from IPython.core.display import Image
import subprocess
def mlab_view(scene, azimuth=153, elevation=62, distance=400, focalpoint=np.array([ 25., 63., 60.]), aa=16):
scene.anti_aliasing_frames = aa
mlab.view(azimuth=azimuth, elevation=elevation, distance=distance, focalpoint=focalpoint)
scene.save('offscreen.png', size=(750, 750))
subprocess.call(["convert", "offscreen.png", "-transparent", "white", "offscreen.png"])
return Image(filename='offscreen.png')
In a high-$\beta$ plasma, and assuming parallel propagation ($k_\parallel >> k_\perp$) the three MHD wave modes of a uniform plasma pertub different components of velocity:
ds = pysac.yt.SACGDFDataset('/home/stuart/VivaData/Slog_p240-0_A10_B005_00001.gdf')
yt : [WARNING ] 2015-12-04 14:48:49,348 'field_units' was overridden by 'dataset_units/density_bg' yt : [WARNING ] 2015-12-04 14:48:49,350 'field_units' was overridden by 'dataset_units/density_pert' yt : [WARNING ] 2015-12-04 14:48:49,354 'field_units' was overridden by 'dataset_units/internal_energy_bg' yt : [WARNING ] 2015-12-04 14:48:49,356 'field_units' was overridden by 'dataset_units/internal_energy_pert' yt : [WARNING ] 2015-12-04 14:48:49,360 'field_units' was overridden by 'dataset_units/mag_field_x_bg' yt : [WARNING ] 2015-12-04 14:48:49,362 'field_units' was overridden by 'dataset_units/mag_field_x_pert' yt : [WARNING ] 2015-12-04 14:48:49,364 'field_units' was overridden by 'dataset_units/mag_field_y_bg' yt : [WARNING ] 2015-12-04 14:48:49,366 'field_units' was overridden by 'dataset_units/mag_field_y_pert' yt : [WARNING ] 2015-12-04 14:48:49,368 'field_units' was overridden by 'dataset_units/mag_field_z_bg' yt : [WARNING ] 2015-12-04 14:48:49,370 'field_units' was overridden by 'dataset_units/mag_field_z_pert' yt : [WARNING ] 2015-12-04 14:48:49,377 'field_units' was overridden by 'dataset_units/velocity_x' yt : [WARNING ] 2015-12-04 14:48:49,379 'field_units' was overridden by 'dataset_units/velocity_y' yt : [WARNING ] 2015-12-04 14:48:49,381 'field_units' was overridden by 'dataset_units/velocity_z' yt : [INFO ] 2015-12-04 14:48:49,420 Parameters: current_time = 1.00339395144 yt : [INFO ] 2015-12-04 14:48:49,421 Parameters: domain_dimensions = [128 128 128] yt : [INFO ] 2015-12-04 14:48:49,423 Parameters: domain_left_edge = [ 781250. 781250. 3664122.1] yt : [INFO ] 2015-12-04 14:48:49,424 Parameters: domain_right_edge = [ 1.99218750e+08 1.99218750e+08 1.58778630e+08] yt : [INFO ] 2015-12-04 14:48:49,426 Parameters: cosmological_simulation = 0.0
from tvtk.api import tvtk
#pysac imports
import pysac.yt
import pysac.analysis.tube3D.tvtk_tube_functions as ttf
import pysac.plot.mayavi_plotting_functions as mpf
from pysac.plot.mayavi_seed_streamlines import SeedStreamline
from pysac.plot.divergingcolourmaps import get_mayavi_colourmap
from pysac.analysis.tube3D.process_utils import get_yt_mlab
### Load in and Config ###
# loaded above
ds = pysac.yt.SACGDFDataset('/home/stuart/VivaData/Slog_p240-0_A10_B005_00001.gdf')
tube_r = 60
#if running this creates a persistant window just get it out of the way!
mlab.options.offscreen = True
fig = mlab.figure(bgcolor=(1, 1, 1))
cg = ds.index.grids[0]
#Slices
cube_slice = np.s_[:,:,:-5]
x_slice = np.s_[:,:,:,:-5]
#Define the size of the domain
xmax, ymax, zmax = np.array(cg['density'].to_ndarray()[cube_slice].shape) - 1
domain = {'xmax':xmax, 'ymax':ymax, 'zmax':zmax}
bfield, vfield = get_yt_mlab(ds, cube_slice, flux=False)
#Create a scalar field of the magntiude of the vector field
bmag = mlab.pipeline.extract_vector_norm(bfield, name="Field line Normals")
yt : [INFO ] 2015-12-04 14:48:49,672 Loading field plugins. yt : [INFO ] 2015-12-04 14:48:49,673 Loaded angular_momentum (8 new fields) yt : [INFO ] 2015-12-04 14:48:49,674 Loaded astro (15 new fields) yt : [INFO ] 2015-12-04 14:48:49,675 Loaded cosmology (22 new fields) yt : [INFO ] 2015-12-04 14:48:49,678 Loaded fluid (63 new fields) yt : [INFO ] 2015-12-04 14:48:49,680 Loaded fluid_vector (95 new fields) yt : [INFO ] 2015-12-04 14:48:49,682 Loaded geometric (111 new fields) yt : [INFO ] 2015-12-04 14:48:49,683 Loaded local (115 new fields) yt : [INFO ] 2015-12-04 14:48:49,685 Loaded magnetic_field (120 new fields) yt : [INFO ] 2015-12-04 14:48:49,687 Loaded my_plugins (120 new fields) yt : [INFO ] 2015-12-04 14:48:49,688 Loaded species (122 new fields)
xc = domain['xmax']/2
yc = domain['ymax']/2
ti = 0
n = 100
surf_seeds = []
for theta in np.linspace(0, 2 * np.pi, n, endpoint=False):
surf_seeds.append([tube_r * np.cos(theta + 0.5 * ti) + xc,
tube_r * np.sin(theta + 0.5 * ti) + yc, domain['zmax']])
seeds = np.array(surf_seeds)
#Add axes:
axes, outline = mpf.add_axes(np.array(zip(ds.domain_left_edge,ds.domain_right_edge)).flatten()/1e8)
#Add seed points to plot:
seed_points = mlab.points3d(seeds[:,0], seeds[:,1], seeds[:,2],
color=(0.231, 0.298, 0.752), scale_mode='none',
scale_factor=1.5)
mlab_view(fig.scene)
/opt/miniconda/envs/thesis/lib/python2.7/site-packages/mayavi/tools/camera.py:288: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if focalpoint is not None and not focalpoint == 'auto':
field_lines = SeedStreamline(seed_points = np.array(seeds))
bmag.add_child(field_lines)
field_lines.actor.mapper.scalar_visibility = False
field_lines.actor.property.color = (0,0,0)
field_lines.actor.property.line_width = 1.5
mlab_view(fig.scene)
pd_seeds = ttf.make_circle_seeds(100, 60, **domain)
fieldlines, surface = ttf.create_flux_surface(bfield.outputs[0], pd_seeds)
surface.output.lines = None
flux_surface = mlab.pipeline.surface(surface.output)
flux_surface.actor.mapper.scalar_visibility = False
flux_surface.actor.property.color = (0.8,0.8,0.8)
#flux_surface.actor.property.line_width = 0
mlab_view(fig.scene)
axes.visible = False
outline.visible = False
flux_surface.actor.property.edge_visibility = True
mlab_view(fig.scene, azimuth = 90, elevation = 75, distance=80, focalpoint=[63, 120, 110], aa=20)
poly_norms = ttf.make_poly_norms(surface.output)
normvec = mlab.pipeline.glyph(poly_norms.output)
normvec.glyph.glyph_source.glyph_source = normvec.glyph.glyph_source.glyph_dict['arrow_source']
normvec.glyph.glyph.scale_mode = 'data_scaling_off'
normvec.glyph.glyph.color_mode = 'color_by_scale'
normvec.glyph.glyph.scale_factor = 5
normvec.glyph.glyph_source.glyph_position = 'tail'
mlab_view(fig.scene, azimuth=85, elevation=80, distance=50, focalpoint=[63, 120, 110], aa=20)
Mumford, S. J., Fedun, V., Erdélyi, R. - The Astrophysical Journal - January 2015 - Volume 799, Issue 1
Generation of Magnetohydrodynamic Waves in Low Solar
Atmospheric Flux Tubes by Photospheric Motions
!/opt/miniconda/envs/mpl14/bin/python python/make_animations.py
$$ \vec{V}(x,y,z,t) = \vec{F}(x,y,z) \ e^{-\left(\frac{x^2}{\Delta x^2} + \frac{y^2}{\Delta y^2} + \frac{z^2}{\Delta z^2}\right)} \sin \left(2\pi \frac{t}{P}\right) $$
(Wedemeyer-Böhm and Rouppe van der Voort 2009)
$$ F_x = A \frac{B_Ax}{x^2 + y^2} \frac{y}{\sqrt{x^2 + y^2}},\\ F_y = - A \frac{B_Ay}{x^2 + y^2} \frac{x}{\sqrt{x^2 + y^2}}. $$
$$ F_x = A \frac{\cos(\theta + \phi)}{\sqrt{x^2 + y^2}},\\ F_y = - A \frac{\sin(\theta + \phi)}{\sqrt{x^2 + y^2}},\\ $$ where, $\theta = \tan^{-1}\left(\frac{y}{x}\right),\ \phi = \tan^{-1}\left(\frac{1}{B_L}\right)$
Calculate wave energy flux from (Leroy 1985).
$$
\vec{F}_{\text{wave}} \equiv \widetilde{p}_k \vec{v} + \frac{1}{\mu_0} \left(\vec{B}_b \cdot \vec{\widetilde{B}}\right) \vec{v} - \frac{1}{\mu_0}\left(\vec{v} \cdot \vec{\widetilde{B}} \right) \vec{B}_b
$$
Decompose onto the flux surfaces in the same manner as velocity.
import td_plotting_helpers as ph
import time_distance_plots as tdp
from matplotlib.image import NonUniformImage
import texfigure
ch4 = texfigure.Manager(None, number=4, base_path='/home/stuart/GitHub/Thesis/thesis/Chapter4/')
figsize = (17,9)
pvel_labels = {'par_label':r'$V_\parallel[$ ms$^{-1}$]',
'perp_label':r'$V_\perp$ [ms$^{-1}$]',
'phi_label':r'$V_\phi$ [ms$^{-1}$]'}
pflux_labels = {'par_label':r'$F^2_\parallel / F^2$ ',
'perp_label':r'$F^2_\perp / F^2$',
'phi_label':r'$F^2_\phi / F^2$'}
post_amp = "A10"
period = "p240"
tube_r = 'r30'
drivers = ['horiz', 'vert', 'Suni', 'Sarch', 'Slog']
exp_facs = [None, None, 'B0', 'B0005', 'B005']
captions = ['Horizontal', 'Vertical', 'Circular', 'Archemedian Spiral', 'Logarithmic Spiral']
figures = {}
for j, (driver, exp_fac, caption) in enumerate(zip(drivers, exp_facs, captions)):
all_times, y, all_spoints = tdp.get_xy(ch4.data_dir, driver, period, post_amp, tube_r, exp_fac)
data, beta_line = tdp.get_data(ch4.data_dir, driver, period, post_amp, tube_r, exp_fac)
va_line, cs_line = tdp.get_speeds(ch4.data_dir, driver, period, post_amp, tube_r, exp_fac)
Fdata, beta_line, avgs = tdp.get_flux(ch4.data_dir, driver, period, post_amp, tube_r, exp_fac)
fd = lambda args: [a.T for a in args]
ph.xxlim = -150
fig, axes = plt.subplots(nrows=3, ncols=2, sharex=True, figsize=figsize)
ph.triple_plot(axes[:,0], all_times, y, *fd(data), beta_line=1./beta_line,
levels=[1.,3.,5.,7.,10.,100.], manual_locations=False, **pvel_labels)
ph.triple_plot(axes[:,1], all_times, y, *fd(Fdata), beta_line=1./beta_line,
levels=[1.,3.,5.,7.,10.,100.], manual_locations=False, cmap='PRGn', **pflux_labels)
for ax in axes[:,0]:
ph.add_phase_speeds(ax, all_times, y, va_line, cs_line, dx_scale=1e6, color='g')
for ax in axes[:,1]:
ph.add_phase_speeds(ax, all_times, y, va_line, cs_line, dx_scale=1e6, color='c')
axes[0,1].legend(bbox_to_anchor=(1.60, 1.05))
#Remove the top two x labels
axes[0,0].set_xlabel('')
axes[1,0].set_xlabel('')
axes[0,1].set_xlabel('')
axes[1,1].set_xlabel('')
fig.tight_layout(h_pad=0.05)
figures[driver] = fig
figures['vert']
(Mumford, Fedun, and Erdélyi 2015)
figures['horiz']
(Mumford, Fedun, and Erdélyi 2015)
figures['Slog']
(Mumford, Fedun, and Erdélyi 2015)
from flux_comparison import make_flux_bar_chart, get_averages
Favgs = get_averages(ch4.data_dir)
fig = make_flux_bar_chart((12,10), ch4.data_dir)
fig
(Mumford, Fedun, and Erdélyi 2015)
from sacconfig import SACConfig
cfg = SACConfig(cfg_file="./python/ch5_config.cfg")
BL = np.array([0.015, 0.05, 0.15, 0.45, 1.5])
fig, ax = plt.subplots(figsize=(9,2), dpi=600)
ax.plot(BL, np.ones(BL.size), 'x', markersize=10, mew=2)
ax.errorbar([0.15], [1], xerr=np.array([[-1*(0.15-1/(6.4-1.6)), 0.15+1/(6.4+1.6)]]).T, mew=2, elinewidth=2)
ax.semilogx()
ax.get_yaxis().set_visible(False)
ax.set_frame_on(False)
ax.get_xaxis().tick_bottom()
ax.xaxis.set_tick_params(width=2)
ax.xaxis.set_tick_params(width=2, which='minor')
ax.xaxis.set_major_formatter(matplotlib.ticker.ScalarFormatter())
ax.xaxis.set_ticks(BL)
xmin, xmax, ymin, ymax = ax.axis()
ax.add_artist(plt.Line2D((xmin, xmax), (ymin, ymin), color='black', linewidth=1.4))
l = ax.set_xlim([0.01, 2.0])
l = ax.set_xlabel(r'$B_L$', fontsize=18)
fig.tight_layout(h_pad=0.01)
fig.savefig('./images/ch5_space.png', transparent=True)
Mumford, S. J. and Erdélyi, R. - Monthly Noticies of the Royal Astronomical Society - March 2015 - Volume 449 Issue 2.
Photospheric Logarithmic Velocity Spirals as MHD Wave Generation Mechanisms
beta = False
def add_triple_phase(ax, tube_r, color='g'):
ps = ph.get_phase_speeds(cfg, tube_r)
for ax0 in ax:
ph.add_phase_speeds(ax0, color=color, **ps)
bl_figures = {}
for j, bl in enumerate(BL):
cfg.exp_fac = bl
fig, ax = plt.subplots(nrows=3, ncols=2, sharex=True, figsize=figsize)
kwargs = ph.get_single_velocity(cfg, 'r30', beta=beta)
kwargs.update(pvel_labels)
ph.triple_plot(ax[:,0], **kwargs)
kwargs = ph.get_single_percentage_flux(cfg, 'r30', beta=beta)
kwargs.update(pflux_labels)
kwargs.update({'cmap': 'PRGn'})
ph.triple_plot(ax[:,1], **kwargs)
#Remove the top two x labels
ax[0,0].set_xlabel('')
ax[1,0].set_xlabel('')
add_triple_phase(ax[:,0], 'r30')
ax[0,1].set_xlabel('')
ax[1,1].set_xlabel('')
add_triple_phase(ax[:,1], 'r30', color='c')
#add_all_bpert(ax, 'r30')
fig.tight_layout(h_pad=0.05)
ax[0,1].legend(bbox_to_anchor=(1.60, 1.05))
bl_figures['B{}'.format(str(bl).replace('.',''))] = fig
bl_figures['B0015']
(Mumford and Erdélyi 2015)
bl_figures['B015']
(Mumford and Erdélyi 2015)
bl_figures['B15']
(Mumford and Erdélyi 2015)
import os
int_periods = np.floor(600./cfg.period)*180
def calc_avgs(tube_r):
AvgsP = np.zeros([3,len(BL)])
for i, bl in enumerate(BL):
cfg.exp_fac = bl
times = np.load(os.path.join(cfg.data_dir, 'Times_{}.npy'.format(cfg.get_identifier())))
max_index = np.argmin(np.abs(int_periods - times))
Fpar, Fperp, Fphi = map(np.load, ph.glob_files(cfg, tube_r, 'LineFlux*Fp*npy'))
#Fpar, Fperp, Fphi = map(np.load, ph.glob_files(cfg, tube_r, '*vp*npy'))
Fpar[np.abs(Fpar)<1e-5], Fperp[np.abs(Fperp)<1e-5], Fphi[np.abs(Fphi)<1e-5] = 0., 0., 0.
Fpar, Fperp, Fphi = Fpar[:max_index,:], Fperp[:max_index,:], Fphi[:max_index,:]
Ftot2 = (Fpar**2 + Fperp**2 + Fphi**2)
Fpar2, Fperp2, Fphi2 = np.array([Fpar, Fperp, Fphi])**2
FparP, FperpP, FphiP = (Fpar2/Ftot2)*100, (Fperp2/Ftot2)*100, (Fphi2/Ftot2)*100
FparP = np.mean(np.ma.masked_array(FparP, np.isnan(FparP)))
FperpP = np.mean(np.ma.masked_array(FperpP, np.isnan(FperpP)))
FphiP = np.mean(np.ma.masked_array(FphiP, np.isnan(FphiP)))
AvgsP[:, i] = FparP, FperpP, FphiP
return AvgsP
fig, axs = plt.subplots(nrows=3, figsize=(11,11), sharex=True)
titles = ["Flux Surface Radius $=156$ km", "Flux Surface Radius $=468$ km", "Flux Surface Radius $=936$ km"]
tubes = []
for i, ax in enumerate(axs):
AvgsP = calc_avgs(cfg.tube_radii[i])
tubes.append(AvgsP)
ax.semilogx()
ax.plot(BL, AvgsP[0], 'o', label=r"$F_\parallel^2$", mew=0, ms=10)
ax.plot(BL, AvgsP[1], '_', label=r"$F_\perp^2$", mew=2, ms=10)
ax.plot(BL, AvgsP[2], 'x', label=r"$F_\phi^2$", mew=2, ms=10)
ax.set_ylabel("% Square Wave Flux")
ax.set_title(titles[i])
ax.xaxis.set_major_formatter(matplotlib.ticker.ScalarFormatter())
ax.xaxis.set_ticks(BL)
ax.set_xlim([0.01, 2.01])
ax.set_ylim([0, 85])
err = np.array([-1*(0.15-1/(6.4-1.6)), 0.15+1/(6.4+1.6)])
ax.fill_betweenx(np.linspace(-5,105), err[0], err[1], alpha=0.3, color='green', linewidth=0)
axs[0].legend(bbox_to_anchor=(1.20, 1.05))
axs[-1].set_xlabel("Logarithmic Spiral Expansion Factor ($B_L$)")
plt.tight_layout()
fig
(Mumford and Erdélyi 2015)
The amplitude is calculated using:
$$
A^2 \propto \frac{1}{P}
$$
with $A=10$ ms$^{-1}$ and $P=240$ s as the reference point.
| Period [seconds] | Amplitude [ms$^{-1}$] |
| $30.0$ | $20\sqrt{2}$ |
| $60.0$ | $20$ |
| $90.0$ | $20\sqrt{\frac{2}{3}}$ |
| $120.0$ | $10\sqrt{2}$ |
| $150.0$ | $4\sqrt{10}$ |
| $180.0$ | $\frac{20}{\sqrt{3}}$ |
| $210.0$ | $20\sqrt{\frac{2}{7}}$ |
| $240.0$ | $10$ |
| $270.0$ | $\frac{20}{3}\sqrt{2}$ |
| $300.0$ | $4\sqrt{5}$ |
from period_amps import periods, str_amps, sim_params
all_periods = sim_params[:10]
periods = periods[:10]
cfg = SACConfig(cfg_file='./python/ch6_config.cfg')
cfg.data_dir = '/home/stuart/GitHub/Thesis/thesis/Chapter6/Data/'
ph.xxlim = 600
p_figures= {}
for i, paf in enumerate(all_periods):
[setattr(cfg, f, getattr(paf, f)) for f in paf._fields]
fig, ax = plt.subplots(nrows=3, ncols=2, sharex=True, figsize=figsize)
kwargs = ph.get_single_velocity(cfg, 'r30', beta=beta)
kwargs.update(pvel_labels)
ph.triple_plot(ax[:,0], **kwargs)
kwargs = ph.get_single_percentage_flux(cfg, 'r30', beta=beta)
kwargs.update(pflux_labels)
kwargs.update({'cmap': 'PRGn'})
ph.triple_plot(ax[:,1], **kwargs)
#Remove the top two x labels
ax[0,0].set_xlabel('')
ax[1,0].set_xlabel('')
add_triple_phase(ax[:,0], 'r30')
ax[0,1].set_xlabel('')
ax[1,1].set_xlabel('')
add_triple_phase(ax[:,1], 'r30', color='c')
#add_all_bpert(ax, 'r30')
fig.tight_layout(h_pad=0.05)
ax[0,1].legend(bbox_to_anchor=(1.6, 1.05))
p_figures['{}'.format(str(paf.period))] = fig
p_figures['30.0']
p_figures['150.0']
p_figures['300.0']
from period_amps import periods, sim_params
sim_params = sim_params[:10]
periods = np.array(periods[:10])
fig, axs = plt.subplots(nrows=3, figsize=(11,11), sharex=True)
titles = ["Flux Surface Radius $=156$ km", "Flux Surface Radius $=468$ km", "Flux Surface Radius $=936$ km"]
tubes = []
for i, ax in enumerate(axs):
AvgsP = ph.get_all_avgs(cfg, cfg.tube_radii[i], sim_params)
tubes.append(AvgsP)
ax.plot(periods, AvgsP[0], 'o', label=r"$F_\parallel^2$", mew=0, ms=12)
ax.plot(periods, AvgsP[1], '_', label=r"$F_\perp^2$", mew=2, ms=12)
ax.plot(periods, AvgsP[2], 'x', label=r"$F_\phi^2$", mew=2, ms=12)
ax.set_ylabel("% Square Wave Flux")
ax.set_title(titles[i])
ax.xaxis.set_major_formatter(matplotlib.ticker.ScalarFormatter())
ax.xaxis.set_ticks(periods)
ax.set_xticklabels(["{}\n[{:n}]".format(p, 600//p) for p in periods])
ax.set_ylim([10, 75])
ax.set_xlim([25, 305])
axs[-1].set_xlabel("Period [s] \n [Number of periods averaged]")
#axs[0].legend(loc=2)
axs[0].legend(bbox_to_anchor=(1.21, 1.05))
plt.tight_layout(h_pad=0.1)
fig
Mumford, S. J. and Erdélyi, R. - Monthly Noticies of the Royal Astronomical Society - March 2015 - Volume 449 Issue 2.
Photospheric Logarithmic Velocity Spirals as MHD Wave Generation Mechanisms
Mumford, S. J., Fedun, V., Erdélyi, R. - The Astrophysical Journal - January 2015 - Volume 799, Issue 1
Generation of Magnetohydrodynamic Waves in Low Solar
Atmospheric Flux Tubes by Photospheric Motions
The SunPy Community, Mumford, S. J., Christe, S., Pérez-Suárez, D., et. al - Computational Science and Discovery - January 2015 - Volume 8 Issue 1.
SunPy: Python for Solar Physics
Freij N., Scullion E. M., Nelson C. J., Mumford S. J., Wedemeyer S., and Erdélyi R. - The Astrophysical Journal - July 2014 - Volume 791, Issue 1, p.61
The Detection of Upwardly Propagating Waves Channeling Energy from the Chromosphere to the Low Corona
Gent, F. A., Fedun, V., Mumford, S. J., Erdélyi, R. - Monthly Notices of the Royal Astronomical Society - October 2013 - Volume 435, Issue 1, p.689-697
Magnetohydrostatic equilibrium - I. Three-dimensional open magnetic flux tube in the stratified solar atmosphere
Nelson, C. J., Doyle, J. G., Erdélyi, R., Huang, Z., Madjarska, M. S., Mathioudakis, M., Mumford, S. J., Reardon, K - Solar Physics - April 2013 - Volume 283, Issue 2, p.307-323.
Statistical Analysis of Small Ellerman Bomb Events